Input path: /home/debian/html/nutritwin/output_llm/664aed6ee2145/input.json
Output path: /home/debian/html/nutritwin/output_llm/664aed6ee2145/output.json
Input text: Ce matin j'ai mangé un yaourt Danone
DB path: __deriveddata__/DerivedObjects/Data/KcalMeDB_fr.sl3
Picto path: __deriveddata__/DerivedObjects/Data/PictoMatcherNetNG_fr.json
Sport grounding path: __deriveddata__/DerivedObjects/Data/DerivedSportMET.json
==================================================================================================================================
Prompt from user: Ce matin j'ai mangé un yaourt Danone
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Identify food consumption or declaration", "Identify the user physical activity", "Answer a nutrition question", "Other intent"], the intents of the prompt: ###Ce matin j'ai mangé un yaourt Danone###.
Format the result in JSON format: {intents: []}.
=========================================================================================
------------------------------ LLM Raw response -----------------------------
```json
{
"intents": ["Identify food consumption or declaration"]
}
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
{
"intents": ["Identify food consumption or declaration"]
}
```
------------------------------------------------------
------------------------ After simplification ------------------------
{ "intents": ["Identify food consumption or declaration"]}
----------------------------------------------------------------------
==================================== Prompt =============================================
Convert this natural language query : """Ce matin j'ai mangé un yaourt Danone""" into an array in JSON of consumed foods and beverages.
Provide a solution without explanation.
Use only the ontology described in this Turtle/RDF model:
"""
@prefix food: .
@prefix rdfs: .
@prefix xsd: .
@prefix owl: .
@prefix prov: .
food: a owl:Ontology ;
rdfs:comment "Definition of the food archetype"@en .
food:name a owl:DatatypeProperty;
rdfs:label "name"@en;
rdfs:comment "Food or drink identifier, the name should not contain information related to quantity or container (like glass...). The cooking mode is not in the name. When the brand is very well-known (ex: Activia, Coca-Cola), the name is equal to the brand. Keep the same language"@en;
rdfs:range xsd:string.
food:quantity a owl:DatatypeProperty ;
rdfs:label "quantity"@en;
rdfs:comment "The quantity of food or drink that is or was consumed. Quantity examples in french: 'un quignon', 'un cornet', 'un verre', 'une tranche', 'une boule', 'un', 'deux', 'trois',... Keep the same language."@en;
rdfs:range xsd:string.
food:cookingMethod a owl:DatatypeProperty ;
rdfs:label "cooking method"@en;
rdfs:comment "The cooking method of food. Keep the same language"@en;
rdfs:range xsd:string.
food:type a owl:DatatypeProperty ;
rdfs:label "type of food"@en;
rdfs:comment "Identify the type of food."@en;
rdfs:range xsd:string.
food:food a food:type ;
rdfs:label "food" .
food:beverage a food:type ;
rdfs:label "beverage" .
food:timeOfTheDay a owl:DatatypeProperty ;
rdfs:label "time of the day"@en;
rdfs:comment "Time of the day when food or drink was consumed."@en;
rdfs:range xsd:string.
food:breakfast a food:timeOfTheDay ;
rdfs:label "breakfast" .
food:lunch a food:timeOfTheDay ;
rdfs:label "lunch" .
food:snacking a food:timeOfTheDay ;
rdfs:label "snacking" .
food:dinner a food:timeOfTheDay ;
rdfs:label "dinner" .
food:brand a owl:DatatypeProperty ;
rdfs:label "Brand"@en;
rdfs:comment "Food or beverage brand. The restaurants are not brand. When the 'brand' is not specified and, the food or beverage is very well-known (like 'Coca-Cola'), provide the brand name in 'brand', otherwise set 'brand' to ''."@en;
rdfs:range xsd:string.
food:company a owl:DatatypeProperty ;
rdfs:label "Company"@en;
rdfs:comment "Product company."@en;
rdfs:range xsd:string.
food:enumEvent a rdfs:Class .
food:event a owl:DatatypeProperty ;
rdfs:label "event"@en;
rdfs:comment "Event of eating or drinking. Each must have an event"@en;
rdfs:range food:enumEvent.
food:intent a food:enumEvent ;
rdfs:label "intent" .
rdfs:comment "When the event should happen"@en.
food:declaration a food:enumEvent ;
rdfs:label "declaration" .
rdfs:comment "When the event has already occured"@en.
food:unknownEvent a food:enumEvent ;
rdfs:label "unknown" ;
rdfs:comment "When the event is unknown in the day"@en.
"""
=========================================================================================
------------------------------ LLM Raw response -----------------------------
```json
[
{
"name": "Danone",
"quantity": "un",
"type": "food",
"timeOfTheDay": "breakfast",
"brand": "Danone",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "Danone",
"quantity": "un",
"type": "food",
"timeOfTheDay": "breakfast",
"brand": "Danone",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "Danone", "quantity": "un", "type": "food", "timeOfTheDay": "breakfast", "brand": "Danone", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Danone', 'quantity': 'un', 'type': 'food', 'timeOfTheDay': 'breakfast', 'brand': 'Danone', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'Danone', 'quantity': 'un', 'type': 'food', 'timeOfTheDay': 'breakfast', 'brand': 'Danone', 'event': 'declaration'}
First try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_GTINRef,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormName LIKE '% danone %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
Second try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_GTINRef,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormAggr LIKE '% danone %' AND V_NormTrademark LIKE '%%'
------------- Found solution (max 20) --------------
Fiz - fiz - - Danone - 0 - 3057640178078 - 3057640178078 - OFF#24304da7060eca2f8b488fda553b929a
Eau - eau - - Danone - 0 - 3068320113777 - 3068320113777 - OFF#a368112e858a7875171326b1b7219c8d
Duo - duo - - Danone - 0 - 5410146411540 - 5410146411540 - OFF#1d532afef14a2f78d7411235c44defd0
Eau - eau - - Danone - 0 - 4909411061548 - 3068320113777 - OFF#e176bc3d62c2c778a332abbc2d02559c
Eau - eau - - Danone - 0 - 3068320119267 - 3068320113777 - OFF#777ddce7b93ca02cf49268c755d2bf13
Skyr - skyr - - Danone - 0 - 00000000000026772226 - 00000000000026772226 - OFF#cc2947e747670c68b2a88eb33ca48481
Skir - skir - - Danone - 0 - 08559431 - 08559431 - OFF#229855d9dbc5113ebe11c246fda98f7a
Vijg - vijg - - Danone - 0 - 5410146416866 - 5410146416866 - OFF#99e12ccf197e5db66ce0659162f8a063
Sana - sana - - Danone - 0 - 5941209014649 - 5941209014649 - OFF#4dee9d6498ba38813ab55876918994e4
Lait - lait - - Danone - 0 - 6111032003298 - 6111032003298 - OFF#59e61a52af338a3745c237649e837d1f
Milk - milk - - Danone - 0 - 8690642101108 - 8690642101108 - OFF#26717712928c4a3d38a970b05717ba77
Skyr - skyr - - Danone - 0 - 44244743 - 00000000000026772226 - OFF#33bf3d0445cbab2000a2d1acf4bb80ce
Skyr - skyr - - Danone - 0 - 3033490004743 - 00000000000026772226 - OFF#4e2f5df02037d4865338f6ac09db5f27
Skyr - skyr - - Danone - 0 - 3033491454080 - 00000000000026772226 - OFF#0187f86d1e42297d7b2dedbe150a10d8
Skyr - skyr - - Danone - 0 - 10330791 - 00000000000026772226 - OFF#e25a67260ef68c8bbc282734d131095f
Skyr - skyr - - Danone - 0 - 03414569 - 00000000000026772226 - OFF#7233f418587784a39fc3d0d3da2f59b6
Skyr - skyr - - Danone - 0 - 2033496445756 - 00000000000026772226 - OFF#82752b0266082d9d701cadcedc5e793f
Skyr - skyr - - Danone - 0 - 0034361454080 - 00000000000026772226 - OFF#2c73bfd38f6ae207fcde4bf8fa94256e
Skyr - skyr - - Danone - 0 - 3033491704642 - 00000000000026772226 - OFF#92fbe50fc69ec708f06886fb08ed94ec
Evian - evian - - Danone - 0 - 0061314000056 - 0061314000056 - OFF#20ea47dd0043f93eaaca565a4e6bb6e2
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "Ce matin j'ai mangé un yaourt Danone", 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Fiz', 'normName': ' fiz ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#24304da7060eca2f8b488fda553b929a', 'quantity': 'un', 'quantityLem': '1', 'pack': ['VX1', 'BI4', 'VA2', 'VA3', 'GOB', 'C3B', 'C33', 'C15', 'SOD', 'VA4', 'VFF'], 'type': 'food', 'gtin': '3057640178078', 'gtinRef': '3057640178078', 'brand': 'Danone', 'time': 'breakfast', 'event': 'declaration', 'serving': 'VX1-100', 'posiNormName': -1}], 'activity': [], 'response': {}}, 'cputime': 2.0688908100128174}
----------------------------------------------------------------------------------
LLM CPU Time: 2.0688908100128174